{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make datasets and test set\n",
    "#### data points are sampled from a curve y= x+ 20*sin(x/10).<br>make sure the shape of the data matrix with which you feed to the tensor correspond to the shape of the tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12e3bb4590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib import animation, rc\n",
    "from IPython.display import HTML\n",
    "%matplotlib inline\n",
    "# Define input data\n",
    "X_data = np.arange(100, step=2.5)\n",
    "y_data = X_data + 20 * np.sin(X_data/10)\n",
    "# Plot input data\n",
    "plt.scatter(X_data, y_data)\n",
    "\n",
    "n_samples = int(100/2.5)\n",
    "# Tensorflow is picky about shapes, so resize\n",
    "X_data = np.reshape(X_data, (n_samples,1))\n",
    "y_data = np.reshape(y_data, (n_samples,1))\n",
    "\n",
    "X_test = np.random.rand(200)*120\n",
    "X_test = X_test.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the graph and run it\n",
    "#### In Graph building stage, python call tensorflow api to create Tensorflow Nodes and connect those Nodes. (at this moment the graph is static)<br>Note that in tensorflow Graph, <FONT COLOR=\"green\">Nodes</FONT> are called <FONT COLOR=\"green\">Operations</FONT> and <FONT COLOR=\"red\">Edges</FONT> connect those Nodes are called <font color=\"red\">tensors</font> \n",
    "#### A fetch on a tensor would require its preceeding Node to activate,  for a Node to activate would require all it's inbound tensors/Edges to be filled with data, recursively all the way to the leaf Nodes (placeholder, variable, constant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "################Graph building stage################\n",
    "# Define placeholders for input\n",
    "X = tf.placeholder(tf.float32, shape=(None, 1))\n",
    "y = tf.placeholder(tf.float32, shape=(None, 1)) \n",
    "\n",
    "# Define variables to be learned\n",
    "with tf.variable_scope(\"linear_regression\"):\n",
    "    W = tf.Variable([[0.]], 'weight')\n",
    "    b = tf.Variable([[10.]], 'bias')\n",
    "    \n",
    "    y_pred = tf.matmul(X, W) + b\n",
    "    loss = 1./2 * tf.reduce_mean((y - y_pred)**2)\n",
    "\n",
    "opt = tf.train.AdamOptimizer(0.1)\n",
    "opt_operation = opt.minimize(loss)\n",
    "####################end of building graph###########\n",
    "animation_fram = []\n",
    "################Run/fetch the graph#################\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.initialize_all_variables())\n",
    "    for _ in xrange(500):\n",
    "        _, loss_val = sess.run([opt_operation, loss], feed_dict={X: X_data, y: y_data})\n",
    "        predict = sess.run(y_pred, feed_dict={X: X_test})\n",
    "        animation_fram.append((X_test, predict, loss_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the fitting process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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dB1wFVAP/BK4k+Mn0ONAFWANc4u5b4rxWfRhS1FqiD0O1isKTlzO9zeww4CXg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\nKGCIiEhK/j/1OHsq3aPqUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12ce13fd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "f, ax = plt.subplots(figsize=(6,4))\n",
    "f.suptitle('Linear Regression Example', fontsize=15)\n",
    "plt.ylabel('Y')\n",
    "plt.xlabel('X')\n",
    "ax.set_xlim(0, 120)\n",
    "ax.set_ylim(0, 120)\n",
    "\n",
    "line_d, = ax.plot([], [], label='fit_line')\n",
    "dots, = ax.plot([], [], 'bo', label='actual_dots')\n",
    "\n",
    "frame_text = ax.text(0.02, 0.95,'',horizontalalignment='left',verticalalignment='top', transform=ax.transAxes)\n",
    "# ax.legend()\n",
    "\n",
    "def init():\n",
    "    line_d.set_data([],[])\n",
    "    dots.set_data([],[])\n",
    "    return (line_d,) + (dots,)\n",
    "\n",
    "def animate(i):\n",
    "   \n",
    "    line_d.set_data(animation_fram[i][0], animation_fram[i][1])\n",
    "\n",
    "    dots.set_data(X_data, y_data)\n",
    "    frame_text.set_text('Timestep = %.1d/%.1d\\nLoss = %.3f' % (i, len(animation_fram), animation_fram[i][2]))\n",
    "    \n",
    "    return (line_d,) + (dots,)\n",
    "\n",
    "anim = animation.FuncAnimation(f, animate, init_func=init,\n",
    "                               frames=len(animation_fram), interval=30, blit=True)\n",
    "\n",
    "HTML(anim.to_html5_video())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
